Alireza Mohammadi (University of Michigan-Dearborn), Hafiz Malik (University of Michigan-Dearborn) and Masoud Abbaszadeh (GE Global Research)

Recent automotive hacking incidences have demonstrated that when an adversary manages to gain access to a safety-critical CAN, severe safety implications will ensue. Under such threats, this paper explores the capabilities of an adversary who is interested in engaging the car brakes at full speed and would like to cause wheel lockup conditions leading to catastrophic road injuries. This paper shows that the physical capabilities of a CAN attacker can be studied through the lens of closed-loop attack policy design. In particular, it is demonstrated that the adversary can cause wheel lockups by means of closed-loop attack policies for commanding the frictional brake actuators under a limited knowledge of the tire-road interaction characteristics. The effectiveness of the proposed wheel lockup attack policy is shown via numerical simulations under different road conditions.

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Model-Agnostic Defense for Lane Detection against Adversarial Attack

Henry Xu, An Ju, and David Wagner (UC Berkeley) Baidu Security Auto-Driving Security Award Winner ($1000 cash prize)!

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Multi-Certificate Attacks against Proof-of-Elapsed-Time and Their Countermeasures

Huibo Wang (Baidu Security), Guoxing Chen (Shanghai Jiao Tong University), Yinqian Zhang (Southern University of Science and Technology), Zhiqiang Lin (Ohio State University)

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FirmWire: Transparent Dynamic Analysis for Cellular Baseband Firmware

Grant Hernandez (University of Florida), Marius Muench (Vrije Universiteit Amsterdam), Dominik Maier (TU Berlin), Alyssa Milburn (Vrije Universiteit Amsterdam), Shinjo Park (TU Berlin), Tobias Scharnowski (Ruhr-University Bochum), Tyler Tucker (University of Florida), Patrick Traynor (University of Florida), Kevin Butler (University of Florida)

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DITTANY: Strength-Based Dynamic Information Flow Analysis Tool for x86...

Walid J. Ghandour, Clémentine Maurice (CNRS, CRIStAL)

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